.. _book_fig_chapter9_fig_photoz_boosting:


Photometric Redshifts by Random Forests
---------------------------------------
Figure 9.16

Photometric redshift estimation using gradient-boosted decision trees, with 100
boosting steps. As with random forests (figure 9.15), boosting allows for
improved results over the single tree case (figure 9.14). Note, however, that
the computational cost of boosted decision trees is such that it is
computationally prohibitive to use very deep trees. By stringing together a
large number of very naive estimators, boosted trees improve on the
underfitting of each individual estimator.



.. image:: ../images/chapter9/fig_photoz_boosting_1.png
    :scale: 100
    :align: center


.. raw:: html
    
    <div class="toggle_trigger"><a href="#">

**Code output:**

.. raw:: html

    </a></div>
    <div class="toggle_container">

.. literalinclude:: fig_photoz_boosting.txt

.. raw:: html

    </div>
    <div class="toggle_trigger" id="start_open"><a href="#">

**Python source code:**

.. raw:: html

    </a></div>
    <div class="toggle_container">

.. literalinclude:: fig_photoz_boosting.py
    :lines: 14-

.. raw:: html

    </div>
    <div align="right">

:download:`[download source: fig_photoz_boosting.py] <fig_photoz_boosting.py>`

.. raw:: html
    
    </div>